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Doctoral education has grown substantially, resulting in a larger and diverse pool of graduates for research. Simultaneously, gender balance in research has become a growing concern, particularly in Europe. Based on the Portuguese case, this paper discusses whether the increase in the pool of graduate talent, considered to be comprised of doctoral graduates, has resulted in a gender balance across different disciplines. The analysis of the evolution of doctoral theses completed by men and women from 1970 to 2016 suggests that, while gender balance has been achieved in terms of participation in doctoral education, horizontal segregation in disciplines persists. A more gender-balanced pool of graduate talent for research across disciplines seems to require more action from Portuguese policymakers and universities, particularly in terms of the implementation of gender equality measures. This is also true for countries where gender equality in doctoral education is a challenge.

Suspension-feeding bivalve molluscs perform important ecological roles by coupling pelagic and benthic systems during their feeding activities. Particle capture, and thus feeding, is dependent on particle encounter and retention on the gill filaments, with several factors influencing this process. Over the past 30 yr, different types of synthetic microspheres have been used to examine aspects of particle capture and ingestion by bivalves. Critics of this work have posited that manufactured particles may contain surfactants, chemicals commonly used in manufacturing to reduce surface tension, that could produce spurious capture and ingestion rates. The goal of this work was to experimentally assess whether the presence of different types of surfactants on manufactured polystyrene particles can result in instantaneous effects on particle capture by the blue mussel Mytilus edulis. The effects of 3 different types of common surfactants (sodium dodecyl sulfate, benzalkonium chloride, Triton-X) on clearance rates (CR) and capture efficiencies (CE) were tested. Results indicated that none of the surfactant treatments had an effect on CR. Treatment with one of the surfactants (Triton-X) significantly lowered CE for 3 µm sized spheres compared to the control spheres (Milli-Q treated). None of the other tested surfactants significantly affected CE when compared to the control treatment. These data add to an understanding of particle handling by bivalves, and suggest that concentrations of surfactants found on commercially available microspheres used for experiments or found in the environment have little immediate effect on feeding processes.

Positive‐social emotions mediate one's cognitive performance, mood, well‐being, and social bonds, and represent a critical variable within therapeutic settings. It has been shown that the upregulation of positive emotions in social situations is associated with increased top‐down signals that stem from the prefrontal cortices (PFC) which modulate bottom‐up emotional responses in the amygdala. However, it remains unclear if positive‐social emotion upregulation of the amygdala occurs directly through the dorsomedial PFC (dmPFC) or indirectly linking the bilateral amygdala with the dmPFC via the subgenual anterior cingulate cortex (sgACC), an area which typically serves as a gatekeeper between cognitive and emotion networks. We performed functional MRI (fMRI) experiments with and without effortful positive‐social emotion upregulation to demonstrate the functional architecture of a network involving the amygdala, the dmPFC, and the sgACC. We found that effortful positive‐social emotion upregulation was associated with an increase in top‐down connectivity from the dmPFC on the amygdala via both direct and indirect connections with the sgACC. Conversely, we found that emotion processes without effortful regulation increased network modulation by the sgACC and amygdala. We also found that more anxious individuals with a greater tendency to suppress emotions and intrusive thoughts, were likely to display decreased amygdala, dmPFC, and sgACC activity and stronger connectivity strength from the sgACC onto the left amygdala during effortful emotion upregulation. Analyzed brain network suggests a more general role of the sgACC in cognitive control and sheds light on neurobiological informed treatment interventions., Human Brain Mapping, 41 (11)

Bivalves are some of the most important suspension feeders in aquatic systems. Much research has been conducted on the feeding mechanisms of adult molluscan suspension feeders, but less is known about the feeding mechanisms of their larval stages. To date, the general consensus is that veligers are restricted to collecting particles 4-20 μm in size and that food selection is indiscriminate within this size range, but this hypothesis remains to be directly tested. Therefore, we experimentally assessed this assumption by quantifying microalgal particle capture rates for the larvae of the Pacific oyster (Crassostrea gigas) when fed five different microalgal species individually and in combination. We then tested whether factors such as cell size affected capture rate and consumption, as well as whether capture rate was affected by the presence of other microalgal species. We found evidence of food preference that was not simply a function of size or relative nutritional quality for C. gigas veligers. Further, we found that food selectivity changed through ontogeny. To our knowledge, the changes in selection that we observed through ontogeny have not been previously reported. Interestingly, there was also a sharp decrease in the variability among replicates in consumption rate as the larvae aged. Whether this is a function of velar structure or larval size remains to be tested. Our results suggest some underlying process resulting in certain species of microalgae being captured and consumed at significantly different rates than others.

This chapter introduces the rationale for looking at how academics are managed, as their status as an elite profession has been questioned. It discusses the multitude of factors at play in managing academics, such as the expansion of higher education, the widespread changes in employment status, the evolving processes to attract and retain talent in the academic profession, the digitalisation and increasing diversity of academic work, the global academic labour market, and the longevity of the academic workforce. It then presents the chapters included in the research handbook on Managing Academics that contribute to a better understanding of how academics are managed at national and institutional levels. A first set of chapters focuses on the changing context for managing academics, a second part discusses the role of academics and other higher education professionals and their relationship, a third group of chapters centres on the theme of gendered academic careers, a fourth collection of chapters delves on the performance of academics, a fifth sequence of chapters looks at human resource management of academics, a sixth section of the handbook analyses mobility and internationalisation of academics, and a final part discusses the theme of age and the generational gaps present in current academia., This work was financially supported by the project POCI-01-0145-FEDER-029427- funded by FEDER, through COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI), and by national funds (OE), through FCT/MCTES.

Suspension-feeding bivalve molluscs have evolved a highly effective mechanism for particle discrimination, which allows them to process efficiently the material to which they are exposed. The mechanisms controlling this preingestive sorting process have been described as either passive or active. Evidence of a passive selection mechanism has been demonstrated in several species of suspension-feeding bivalves. In contrast, to date there has been no evidence that active selection mechanisms underlay particle selection in bivalves, although the possibility of such mechanisms has been hypothesized numerous times. The present study was designed to examine active selection on the gill of the eastern oyster Crassostrea virginica. The gill was chosen for study because in oysters this organ is involved in particle selection. Two in vivo assays were designed in which the gill was exposed to dissolved cell exudate or extracts from the microalga Tetraselmis chuii, and the transport of polystyrene microspheres (25 µm) by the frontal cilia of the ordinary filament quantified by means of video endoscopy. Results demonstrated that the addition of exudates or extracts of T. chuii cells had no significant effect on the percentage of particles being transported dorsally (likely ingested) or ventrally (more likely rejected), and no differences in the number of particles in the ventral groove between control and experimental treatments. Results of follow-up experiments using covalently bound neoglycoproteins commonly found on cell surfaces of microalgal species demonstrated differences in transport of captured microspheres (10 µm, carboxylated) depending on the sugar type. Although chemoreception cannot be completely ruled out, these findings further indicate that physicochemical properties of particles, and not an active behavioral or physiological response (i.e., chemoreception of dissolved metabolites) by the animal, mediate particle selection in oysters.

This article is a preprint and has not been certified by peer review. It was eventually published online on 14 June 2021 by American Chemical Society (ACS Publications) as: Cho, E. et al. (2021) 'Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites', Journal of Chemical Information and Modeling, 61 (6), pp. 3058 - 3073. doi: 10.1021/acs.jcim.1c00449. Data Availability Statement: The trajectories of Mpro simulations and models of the metastable states can be obtained from the corresponding author. Jupyter-notebooks to generate Markov State Models can be downloaded from 10.6084/m9.figshare.14343725 bioRxiv posts many COVID19-related papers. A reminder: they have not been formally peer-reviewed and should not guide health-related behavior or be reported in the press as conclusive. β-coronaviruses alone have been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a back-up against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensible role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all non-redundant ligand-binding sites available for SARS-CoV2, SARS-CoV and MERS-CoV Mpro. Extensive adaptive sampling has been used to explore conformational dynamics employing convolutional variational auto encoder-based deep learning, and investigates structural conservation of the ligand binding sites using Markov state models across β-coronavirus homologs. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-coronavirus homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor. U.S. Department of Energy, Office of Science, through the Advanced Scientific Computing Research (ASCR), under contract number DEAC05-00OR22725 and the Exascale Computing Project (ECP) (17-SC-20-SC). BI would like to acknowledge the COVID-19 pump-priming grant from the University of Huddersfield for funding computing resources for analysis.

Internationalisation has gained prominence in debates on higher education and can be analysed from different perspectives, including cross-border higher education. Cross-border higher education entails relevant challenges, namely regarding its quality. This paper intends to discuss, based on a literature review, the link between cross-border higher education and quality assurance, namely the role quality assurance may have in contributing to a quality cross-border educational offer. 79 documents (indexed in Scopus or Web of Science) published between 1998 and 2019 were content analysed. The results show that both internal and external quality assurance are relevant topics when discussing cross-border higher education and a means to stimulate the overall quality of this educational offer. Moreover, to be effective, quality assurance must rely on cooperation and mutual trust between importing and exporting countries, including their quality assurance agencies.

The goal of this article was to use a systematic review of studies on the larval stages of gastropods reared to metamorphosis to determine whether there are general patterns for the effects of temperature, rearing density, and food availability on larval development and performance among species, major taxa, and modes of development. Most studies did not include sufficient metadata to be included in many of the analyses. For all analyses, there were differences among major groups of taxa in terms of response to the considered variables. Increased temperature was frequently correlated with decreased development time and increased growth but often not for the same taxa. Increased larval density was generally correlated with increased development time, but again, the patterns were not consistent across taxa. The most consistent pattern was the positive correlation between per capita food availability and larval growth. In all but two cases, patterns for the most studied species, Crepidula fornicata, were opposite those of other caenogastropods. This indicates that caution should be used when drawing general patterns among species based on studies of C. fornicata. Among lecithotrophs, the vetigastropod Haliotis rufescens was the most studied. In this case, patterns found for this species were similar to those for all other vetigastropods; however, few species outside the genus Haliotis have been studied. Increased temperature was associated with reduced survivorship and, in the most studied clade, the Vetigastropoda, reduced time to metamorphosis, which suggests that there may be an energetic cost to more rapid development or physiological mechanisms for coping with heat stress. Curiously, increased larval density was associated with increased survivorship for lecithotrophs. In several cases, however, there were too few studies, or the studies that were found did not provide enough metadata to be included in analyses. Although some patterns emerged from existing research on gastropod larvae, studies on a more diverse set of species that report all metadata are required for cross-study comparisons, which are crucial for drawing robust general conclusions.

Suspension-feeding bivalve molluscs are foundation species in coastal intertidal systems. The selective feeding capabilities of these animals can have a large influence on phytoplankton communities and nutrient flow to the benthos. Particle selection, including the types of particles chosen for ingestion and the possible mechanisms mediating selection, has been studied extensively and reported in the literature. To date, however, the possible mechanisms mediating these selective processes have remained elusive. Generally, the focus on a few key commercial species, and their demonstrated range of selective capabilities, has made it difficult to design studies that elucidate the mechanisms behind particle selection. This review focuses on key research that has been carried out in the last 20 y toward better understanding the mechanism that underlays selective capture and ingestion of particles in this important group of animals. Recently, work has been completed which has advanced the field in pointing to a passive mechanism as a mediator of selection, with the interactions between the physicochemical properties of particles and the mucus covering the pallial organs most likely mediating food choice. Although no strong evidence for an immediate, active mechanism which underlies particle selection was found, avenues for future research are suggested in this review. The possible mechanisms that control capture, including qualitative precapture selection, are also summarized and discussed in depth. Methodological considerations for rigorous experiments to advance the field are also discussed, including suggestions of general guidelines for experimental designs, which will allow better comparison of findings across studies.

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) – a measure of the overall genetic risk an individual carries for a disorder – to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10−5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10−5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs., Highlights • The polygenic risk score (PRS) for autism is predicted from grey matter brain volumes • The PRS for schizophrenia is also predicted from grey matter brain structure • A multivariate approach to detect patterns of grey matter is superior to univariate • Brain volumes are not predictive of PRS for ADHD and bipolar disorder

The capabilities of suspension-feeding bivalve molluscs to discriminately ingest particles are well known. Physicochemical properties of particles have been shown to play a role in mediating post-capture selection in these animals. In particular, particle surface charge and wettability, a proxy for hydrophobic-hydrophilic forces, can be used by different bivalve species as determinants of selection. How physicochemical factors of particles affect their capture, however, have been little studied. To investigate such interactions, a series of adhesion assays and particle capture experiments were designed. First, mucus from the gills of the blue mussel, Mytilus edulis (Linnaeus 1758), and the bay scallop, Argopecten irradians (Lamarck 1819), were isolated separately, adsorbed onto glass microscope slides, and incubated with polystyrene microspheres (10 μm) that were either untreated or covalently bound to different neoglycoproteins (NGP; N-acetyl-glucosamine, D-mannose), or bovine serum albumin (BS...

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia. EEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration - the deviant stimulus was twice the duration of the standard; 2) monaural gap - deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90° away from the standards. We used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GP regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R2 = 0.53, p = 0.0006)., Highlights • The diagnostic utility of multiple auditory oddball stimulus paradigms is assessed. • Greatest classification accuracy was achieved using a monaural gap stimulus paradigm. • The full post-stimulus epoch contains relevant discriminatory components.

Unsupervised learning approaches, such as Partial Least Squares, can be used to investigate relationships between multiple sources of data, such as neuroimaging and behavioural data. In cases of high-dimensional datasets with limited number of examples (e.g. neuroimaging data) there is a need for regularisation to enable the solution of the ill-posed problem and prevent overfitting. Different approaches have been proposed to optimise the regularisation parameters in unsupervised models, however, so far, there has been no comparison between the different approaches using the same data. In this work, two optimisation frameworks (i.e. a permutation and a train/test framework) were compared using sparse PLS to investigate associations between brain connectivity and behaviour data. Both frameworks were able to identify at least one brain-behaviour associative effect. A second brain-behaviour effect was only found using the train/test framework. More importantly, the results show that the multivariate associative effects found with the train/test framework generalise better to new data, suggesting that results based on the permutation framework should be carefully interpreted.

There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales by intracranial electroencephalography and fMRI. By comparing individual features and summary descriptions of intracranial EEG activity we determined which best predict fMRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. We also then examine the specificity of this relationship to spatial colocalisation of the two signals.We acquired electrocorticography and fMRI simultaneously (ECoG-fMRI) in the sensorimotor cortex of 3 patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was colocalised with fMRI measures across all subjects. The best model of fMRI changes was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than a model based on the classical frequency bands both during task and rest periods or models derived on a summary of cross spectral changes (e.g. ‘root mean squared EEG frequency’). This suggests that the region specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual–spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks., Highlights • We adapt DCM for use in neurofeedback experiments. • Participants can control a DCM-based neurofeedback signal. • Real-time DCM allows for voluntary control over brain connectivity.

Highlights ► New (post-hoc) procedure for approximating the model evidence is applied to deterministic bilinear dynamic causal models for fMRI. ► Post-hoc procedure is based on optimising only the largest model of a set of models. ► Good agreement found between free energy bound and post-hoc approximations to the model evidence and model parameters. ► Post-hoc approach reduces computational time immensely and can be applied to any nested models., Dynamic causal modelling (DCM) was originally proposed as a hypothesis driven procedure in which a small number of neurobiologically motivated models are compared. Model comparison in this context usually proceeds by individually fitting each model to data and then approximating the corresponding model evidence with a free energy bound. However, a recent trend has emerged for comparing very large numbers of models in a more exploratory manner. This led Friston and Penny (2011) to propose a post-hoc approximation to the model evidence, which is computed by optimising only the largest (full) model of a set of models. The evidence for any (reduced) submodel is then obtained using a generalisation of the Savage-Dickey density ratio (Dickey, 1971). The benefit of this post-hoc approach is a huge reduction in the computational time required for model fitting. This is because only a single model is fitted to data, allowing a potentially huge model space to be searched relatively quickly. In this paper, we explore the relationship between the free energy bound and post-hoc approximations to the model evidence in the context of deterministic (bilinear) dynamic causal models (DCMs) for functional magnetic resonance imaging data.

The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

Previous studies using combined electrical and hemodynamic measurements of brain activity, such as EEG and (BOLD) fMRI, have yielded discrepant results regarding the relationship between neuronal activity and the associated BOLD response. In particular, some studies suggest that this link, or transfer function, depends on the frequency content of neuronal activity, while others suggest that total neuronal power accounts for the changes in BOLD. Here we explored this dependency by comparing different frequency-dependent and -independent transfer functions, using simultaneous EEG-fMRI. Our results suggest that changes in BOLD are indeed associated with changes in the spectral profile of neuronal activity and that these changes do not arise from one specific spectral band. Instead they result from the dynamics of the various frequency components together, in particular, from the relative power between high and low frequencies. Understanding the nature of the link between neuronal activity and BOLD plays a crucial role in improving the interpretability of BOLD images as well as on the design of more robust and realistic models for the integration of EEG and fMRI.

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.